Deep learning has made significant impacts on multi-view stereo systems. State-of-the-art approaches typically involve building a cost volume, followed by multiple 3D convolution operations to recover the input image's pixel-wise depth. While such end-to-end learning of plane-sweeping stereo advances public benchmarks' accuracy, they are typically very slow to compute. We present MVS2D, a highly efficient multi-view stereo algorithm that seamlessly integrates multi-view constraints into single-view networks via an attention mechanism. Since MVS2D only builds on 2D convolutions, it is at least 4x faster than all the notable counterparts. Moreover, our algorithm produces precise depth estimations, achieving state-of-the-art results on challenging benchmarks ScanNet, SUN3D, and RGBD. Even under inexact camera poses, our algorithm still out-performs all other algorithms. Supplementary materials and code will be available at the project page: https://zhenpeiyang.github.io/MVS2D

该论文描述了一种高度有效的多视图立体算法，该算法通过注意机制将多视图约束集成到单视图网络中，仅构建于2D卷积，速度至少比所有其他同类算法快2倍，并在各种挑战性基准上实现了精确的深度估计和3D重建效果，同时在有不精确相机姿态时具有最优表现。

MVS2D: 基于注意力驱动的2D卷积的高效多视图立体重建